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Record W4404688049 · doi:10.1109/lmwt.2024.3503572

Advanced Neural Space Mapping-Based Inverse Modeling Method for Microwave Filter Design

2024· article· en· W4404688049 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Microwave and Wireless Technology Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicGeodetic Measurements and Engineering Structures
Canadian institutionsCarleton University
FundersBeijing Municipal Natural Science FoundationNational Natural Science Foundation of China
KeywordsSpace mappingInverseMicrowaveInverse filterFilter (signal processing)Space (punctuation)Computer scienceArtificial neural networkAlgorithmMathematicsArtificial intelligenceComputer visionGeometryTelecommunications

Abstract

fetched live from OpenAlex

This letter proposes an advanced neural space mapping (NSM)-based inverse modeling method and its applications to microwave filter design. For the first time, the NSM method is introduced into inverse microwave modeling with input dimensional reduction (IDR). By using the Fourier transform and its low-frequency subspaces, we convert the S-parameter curve into a signal spectrum where the energy is concentrated in the low-frequency range, to reduce the dimension of the inverse model. We also propose a two-stage training algorithm for the NSM-based inverse model, along with its application methodology for microwave filter design. Two microwave filter design examples are presented to demonstrate the feasibility of the proposed method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.227
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it